IF 2.4 2区数学

Journal of Differential Equations Pub Date : 2024-09-24 DOI: 10.1016/j.jde.2024.09.036

引用次数: 0

Generalized Frank characterizations of Muckenhoupt weights and homogeneous ball Banach Sobolev spaces 穆肯霍普特权重和同质球巴纳赫索波列夫空间的广义弗兰克特性

IF 1.5 1区数学

Advances in Mathematics Pub Date : 2024-09-24 DOI: 10.1016/j.aim.2024.109957

引用次数: 0

Correlation inequalities for linear extensions 线性扩展的相关不等式

IF 1.5 1区数学

Advances in Mathematics Pub Date : 2024-09-24 DOI: 10.1016/j.aim.2024.109954

引用次数: 0

Mathematical modeling and numerical multigoal-oriented a posteriori error control and adaptivity for a stationary, nonlinear, coupled flow temperature model with temperature dependent density 静态、非线性、密度随温度变化的耦合流动温度模型的数学建模和面向多目标的后验误差控制与适应性数值计算

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2024-09-24 DOI: 10.1016/j.camwa.2024.09.017

引用次数: 0

On Haagerup noncommutative quasi Hp(A) spaces 论 Haagerup 非交换准 Hp(A) 空间

IF 1.2 3区数学

Journal of Mathematical Analysis and Applications Pub Date : 2024-09-24 DOI: 10.1016/j.jmaa.2024.128905

引用次数: 0

Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning 通过分布式学习实现三维点云信号的高效非参数估计

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2024-09-24 DOI: 10.1080/10618600.2024.2406301

Guannan Wang, Yuchun Wang, Annie S. Gao, Li Wang

引用次数: 0

Network inference using mutual information rate, statistical tests and amplitude-phase modulated surrogate data 利用互信息率、统计检验和振幅相位调制代用数据进行网络推断

IF 5.3 1区数学

Chaos Solitons & Fractals Pub Date : 2024-09-24 DOI: 10.1016/j.chaos.2024.115554

{"title":"Network inference using mutual information rate, statistical tests and amplitude-phase modulated surrogate data","authors":"","doi":"10.1016/j.chaos.2024.115554","DOIUrl":"10.1016/j.chaos.2024.115554","url":null,"abstract":"<div><div>In this paper, we propose a new method to infer connectivity in networks using the mutual information rate (MIR), statistical tests and amplitude-phase modulated surrogate data (APMSD). The method is addressing the case where one wants to infer the structure of the network when the equations of motion and the coupling adjacency matrix are known, that is the reverse-engineering problem. It is based on the computation of MIR and statistical, hypothesis tests to infer network connectivity, introducing a new method to generate surrogate data, called the APMSD method, that removes correlation and phase synchronisation in the recorded signals, by randomising their amplitudes and instantaneous phases. The proposed method compares MIR of pairs of signals from the network with the MIR values of pairs of APMSD generated from the signals. We discuss the mathematical aspects of the APMSD method and present numerical results for networks of coupled maps, Gaussian-distributed correlated data, coupled continuous deterministic systems, coupled stochastic Kuramoto systems and for dynamics on heterogeneous networks. We show that in all cases, the method can find at least one pair of percentages of randomisation in amplitudes and instantaneous phases that leads to perfect recovery of the initial network that was used to generate the data. The importance of our method stems from the analytic signal concept, introduced by Gabor in 1946 and Hilbert transform as it provides us with a quantification of the contribution of amplitude (linear or nonlinear) correlation and phase synchronisation in the connectivity among nodes in a network. Our method shows great potential in recovering the network structure in coupled deterministic and stochastic systems and in heterogeneous networks with weighted connectivity.</div></div>","PeriodicalId":9764,"journal":{"name":"Chaos Solitons & Fractals","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0960077924011068/pdfft?md5=11f3b507197a3e610700c30ca13b231a&pid=1-s2.0-S0960077924011068-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continued fractions for q-deformed real numbers, {−1,0,1}-Hankel determinants, and Somos-Gale-Robinson sequences q 个变形实数的连续分数、{-1,0,1}-汉克尔行列式和索莫斯-盖尔-罗宾逊序列

IF 1 3区数学

Advances in Applied Mathematics Pub Date : 2024-09-24 DOI: 10.1016/j.aam.2024.102788

引用次数: 0

A mixed immersed finite element method for fourth-order interface problems on surfaces 表面四阶界面问题的混合沉浸式有限元法

IF 2.9 2区数学

Computers & Mathematics with Applications Pub Date : 2024-09-24 DOI: 10.1016/j.camwa.2024.09.012

引用次数: 0

Zig-zag Eulerian polynomials 之字形欧拉多项式

IF 1 3区数学

European Journal of Combinatorics Pub Date : 2024-09-24 DOI: 10.1016/j.ejc.2024.104073

{"title":"Zig-zag Eulerian polynomials","authors":"","doi":"10.1016/j.ejc.2024.104073","DOIUrl":"10.1016/j.ejc.2024.104073","url":null,"abstract":"<div><div>For any finite partially ordered set <span><math><mi>P</mi></math></span>, the <span><math><mi>P</mi></math></span>-Eulerian polynomial is the generating function for the descent number over the set of linear extensions of <span><math><mi>P</mi></math></span>, and is closely related to the order polynomial of <span><math><mi>P</mi></math></span> arising in the theory of <span><math><mi>P</mi></math></span>-partitions. Here we study the <span><math><mi>P</mi></math></span>-Eulerian polynomial where <span><math><mi>P</mi></math></span> is a naturally labeled zig-zag poset; we call these <em>zig-zag Eulerian polynomials</em>. A result of Brändén implies that these polynomials are gamma-nonnegative, and hence their coefficients are symmetric and unimodal. The zig-zag Eulerian polynomials and the associated order polynomials have appeared fleetingly in the literature in a wide variety of contexts—e.g., in the study of polytopes, magic labelings of graphs, and Kekulé structures—but they do not appear to have been studied systematically.</div><div>In this paper, we use a “relaxed” version of <span><math><mi>P</mi></math></span>-partitions to both survey and unify results. Our technique shows that the zig-zag Eulerian polynomials also capture the distribution of “big returns” over the set of (up-down) alternating permutations, as first observed by Coons and Sullivant. We develop recurrences for refined versions of the relevant generating functions, which evoke similarities to recurrences for the classical Eulerian polynomials. We conclude with a literature survey and open questions.</div></div>","PeriodicalId":50490,"journal":{"name":"European Journal of Combinatorics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0195669824001586/pdfft?md5=899109a09f3bf833016e01aae31a7980&pid=1-s2.0-S0195669824001586-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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